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<p>Severe weather events such as thunderstorms are responsible for deaths and asset damage. Lightning discharge is the most common phenomenon in thunderstorms, which causes death and losses. This review shows the research that has been done on lightning over the Indian region. The severe events are analyzed and simulated using the Weather Research Forecasting model (WRF). The different microphysical schemes, such as WSM-6, NSSL-2, Morrison, and Thomson, have been mainly used for analyzing the events. The NSSL-2 performed well in simulating the lightning events for the lightning potential index (LPI) but has limitations. It is not able to produce cloud-to-ground and intercloud lightning as compared to observations. The lightning parameterization scheme works efficiently in providing CG and IC with some discrepancies. The model time step is critical for understanding the events that are integrated into microphysical schemes. The model simulated results have been validated using reanalysis datasets such as ERA5 and IMDAA. The lightning flash rates simulated by the model are validated using ground observational datasets such as IITM–LDN. The thunderstorm indices with optimal threshold can be used to predict the thunderstorm. Model skill scores like the true statistics score and Heidke skill score are used to assess a model’s accuracy. Recent studies on applications of AI/ML on lightning prediction show promising results.</p>

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Unveiling India’s lightning patterns through satellite-based climatology and numerical modelling of severe weather events: A review

  • 期刊论文

  • 发表日期：

    2025年1月27日

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  • Unashish Mondal,
  • Anish Kumar,
  • S K Panda,
  • S Shreelekshmi,
  • Bijit Kumar Banerjee,
  • Devesh Sharma,
  • Someshwar Das

摘要

Severe weather events such as thunderstorms are responsible for deaths and asset damage. Lightning discharge is the most common phenomenon in thunderstorms, which causes death and losses. This review shows the research that has been done on lightning over the Indian region. The severe events are analyzed and simulated using the Weather Research Forecasting model (WRF). The different microphysical schemes, such as WSM-6, NSSL-2, Morrison, and Thomson, have been mainly used for analyzing the events. The NSSL-2 performed well in simulating the lightning events for the lightning potential index (LPI) but has limitations. It is not able to produce cloud-to-ground and intercloud lightning as compared to observations. The lightning parameterization scheme works efficiently in providing CG and IC with some discrepancies. The model time step is critical for understanding the events that are integrated into microphysical schemes. The model simulated results have been validated using reanalysis datasets such as ERA5 and IMDAA. The lightning flash rates simulated by the model are validated using ground observational datasets such as IITM–LDN. The thunderstorm indices with optimal threshold can be used to predict the thunderstorm. Model skill scores like the true statistics score and Heidke skill score are used to assess a model’s accuracy. Recent studies on applications of AI/ML on lightning prediction show promising results.